Cengiz Zopluoglu

Profile picture of Cengiz Zopluoglu
Associate Professor
Administrator Licensure Program, College of Education, Education Policy and Leadership, Educational Leadership, LEADS, Quantitative Research Methods in Education
Phone: 541-346-3578
Office: 102R Lokey Education Building


Cengiz Zopluoglu is an Associate Professor in the Department of Educational Methodology, Policy, and Leadership (EMPL) at the University of Oregon. His research interests are centered around item response theory, psychometrics, latent variable modeling, and integrating machine learning methods into educational measurement. Dr. Zopluoglu has published and contributed numerous articles on psychometrics and statistical methods, and their applications in educational and behavioral sciences. Dr. Zopluoglu has taught advanced courses on psychometrics and statistical methodology including General Linear Models, Item Response Theory, Measurement and Psychometric Theory, Categorical Data Analysis, and Data Analysis with R in Educational and Behavioral Research. Dr. Zopluoglu holds a masters and doctorate degrees in Quantitative Methods in Education from the Department of Educational Psychology, University of Minnesota. Before joining the faculty at the University of Oregon, Dr. Zopluoglu served seven years as a faculty in the Research, Measurement, and Evaluation Program in the Department of Educational and Psychological Studies (EPS) at the University of Miami.


Ph.D., 2013, University of Minnesota, Twin Cities, MN
Major: Educational Psychology (Quantitative Methods in Education)

M.A., 2009, University of Minnesota, Twin Cities, MN
Major: Educational Psychology (Quantitative Methods in Education)

B.A., 2005, Abant Izzet Baysal University, Bolu, Turkey
Major: Mathematics Education (K-8)

Honors and Awards

2012 – 2013 Graduate Student Research Award, University of Minnesota

2007 – 2013 Fellowship for Graduate Education in the U.S., Ministry of National Education, Turkey


Park, S.E., Ahn, S., & Zopluoglu, C. (in press). Differential item functioning effect size from the multigroup confirmatory factor analysis for a meta-analysis: A simulation study.Educational and Psychological Measurement. https://doi.org/10.1177/0013164420925885

Zopluoglu, C. (2020). A Finite mixture item response theory model for continuous measurement outcomes. Educational and Psychological Measurement, 80(2), 346–364.https://doi.org/10.1177/0013164419856663

Zopluoglu, C. (2019). Computation of response similarity index M4 in R under the dichotomous and nominal item response models. International Journal of Assessment Tools in Education, 6 (5), 1-19. https://doi.org/10.21449/ijate.527299

Zopluoglu, C. (2019). Detecting examinees with item preknowledge in large-scale testing using Extreme Gradient Boosting (XGBoost). Educational and Psychological Measurement, 79(5), 931–961. https://doi.org/10.1177/0013164419839439

Kohli, N., Peralta, Y., Zopluoglu, C., & Davison, M.L. (2018). A note on estimating single-class piecewise mixed-effects models with unknown change points, International Journal of Behavioral Development, 42(5), 518-524. https://doi.org/10.1177/0165025418759237

Sideridis, G.D., & Zopluoglu, C. (2018). Validation of response similarity analysis for the detection of academic cheating: An experimental study. Journal of Applied Measurement, 19(1), 59-75.

Zopluoglu, C., & Davenport, E.C. (2017). A note on using eigenvalues in dimensionality assessment. Practical Assessment, Research & Evaluation, 22 (7), 1-10. http://www.pareonline.net/getvn.asp?v=22&n=7

Myers, N. D., Ahn, S., Lu, M., Celimli, S., & Zopluoglu, C. (2017). Reordering and reflecting factors for simulation studies with exploratory factor analysis. Structural Equation Modeling: A Multidisciplinary Journal, 24(1), 112-128. https://doi.org/10.1080/10705511.2016.1230721

Zopluoglu, C. (2016). Classification performance of answer-copying indices under different types of IRT models. Applied Psychological Measurement, 40(8), 592-607. https://doi.org/10.1177%2F0146621616664724

Kohli, N., Harring J.R., & Zopluoglu, C. (2016). A finite mixture of nonlinear random coefficient models for continuous repeated measures data. Psychometrika, 81(3), 851-880. https://doi.org/10.1007/s11336-015-9462-0

Zopluoglu, C. (2015). Evaluating the sampling performance of exploratory and cross-validated DETECT procedure with imperfect models. Multivariate Behavioral Research, 50(6), 632-644. https://doi.org/10.1080/00273171.2015.1070708

Kohli, N., Hughes, J., Wang, C., Zopluoglu, C., & Davison, M. (2015). Fitting a linear-linear piecewise growth mixture model with unknown knots: A comparison of two common approaches to inference. Psychological Methods, 20(2), 259-275 http://dx.doi.org/10.1037/met0000034

Kohli, N., Sullivan, A., Sadeh, S., & Zopluoglu, C. (2015). Longitudinal mathematics development of students with learning disabilities and students without disabilities: A comparison of linear, quadratic, and piecewise linear mixed effects models. Journal of School Psychology, 53(2),105-120. https://doi.org/10.1016/j.jsp.2014.12.002


Detecting fraud in large-scale testing. Test security has become an essential topic in the past two decades as the results of high-stakes testing are increasingly used to make critical educational and resource allocation decisions. Concurrently, the incentives to engage in testing fraud have also increased. Consequently, the companies that develop and administer such tests, school districts, and state departments of education have grown concerned about the validity (i.e., accuracy) of reported test scores. Although ideally,  test fraud should be prevented, it is difficult to know when fraud occurs (i.e., during or following test administration). Therefore, there has been an increased need for statistical methods that can be used to screen large-scale item response data to identify irregularities. Dr. Zopluoglu’s work in this area focuses on developing new methods to detect test fraud (integrating machine learning algorithms) as well as evaluating the performance of currently existing methods.

Continous Response Model. Continuous Response Model (CRM; Samejima, 1973), has not received as much attention in the IRT literature as the dichotomous and polytomous models and is somewhat underutilized in practice. We encounter continuous response outcomes more frequently than thought in certain educational and behavioral settings, and CRM is a viable, and valuable, psychometric model to make inferences about the items and people with continuous response outcomes. Dr. Zopluoglu’s work in this area focuses on technical aspects of CRM, its calibration, and practical applications.